The Harris Social Impact Fellowship

The Behavioral Insights and Parenting Lab (BIP Lab) uses cutting-edge research methods to understand how behavioral insights can be used to leverage parental investments to promote children’s development and reduce social and economic inequality. Fellows at the BIP Lab will work at the forefront of this mission, pioneering new applications of machine learning and AI in social science research.

Fellows will support the BIP Lab with producing applied machine learning and artificial intelligence methodologies using the Lab's rich datasets of parent surveys, recorded parent-child interactions, and digital program engagement data, such as:

• AI-Powered Survey Simulation. Contributing to the development of a survey “AI panel” model, code, and working simulation of responses to a designated in-survey experiment to predict generates predictive survey responses based on existing datasets of real parental survey.

• Video Analysis of Parent-Child Interactions. Contributing to the development of an AI analysis model prototype for coding set sample of video-recorded parent-child interactions, such as verbal and physical parent-child engagements.

• Analysis of Digital Program Engagement. Contributing to the creation of an AI analysis prototype for coding set sample of parent-program API interactions. More specifically, using AI to code and analyze parent interactions within an SMS text-based program to evaluate how parents engage with digital parenting tools.

Together, these projects will position fellows as pioneers in applying AI to social science research, equipping the BIP Lab to conduct rapid, large-scale analysis and strengthening the evidence base for programs that support parents and children. 

The Crime Lab designs, tests, and scales data-driven innovations to improve the public sector's response to America's gun violence crisis and a criminal justice system that is not truly just. Fellows at the Crime Lab will contribute directly to this work, applying rigorous data analytics and data science to research that informs two flagship initiatives:

The Policing Leadership Academy (PLA). A violence reduction initiative that aims to prevent violence, support officers, and improve fairness and effectiveness in policing by providing a five-month robust management and leadership education to police at the commander level at no cost. The initiative develops stronger police management practices in high-violence cities nationwide and globally, focusing on many of America's highest-violence geographies, and its practices are rigorously evaluated to measure their effectiveness.

Fellows can expect to support these projects with:

• Statistical Modeling and Evaluation: Building and rigorously evaluating statistical models using best practices in machine learning and statistical inference.

• Applied Research and Analysis: Implementing analytical approaches to address specific research questions and translating findings to a range of audiences.

• Data Infrastructure and Processing: Processing large and complex research and administrative datasets, and contributing to the design, implementation, and validation of efficient and reproducible data pipelines.

This is hands-on, high-stakes research work with direct implications for how communities and institutions respond to violence across the country.

The Energy and Environment Lab partners with government agencies and the private sector to identify, rigorously evaluate, and scale programs and policies that reduce pollution, conserve natural resources, improve environmental outcomes, and ensure access to reliable and affordable energy.

Fellows at the E&E Lab will work directly on that mission, contributing to collaborative research projects implemented by the E&E Lab in partnership with government agencies such as the U.S. Environmental Protection Agency that utilize applied economics, randomized control trials (RCTs), machine learning, and cutting-edge monitoring technology to develop and test cost-effective solutions to high-stake environmental challenges.

Fellows can expect to support these projects with:

• Field experiments (RCTs). Designing and implementing practically and politically feasible randomized controlled trials by leveraging real-time data for monitoring and enforcement of environmental policies.

• Machine Learning and Analytical Modeling. Developing machine learning models to inform policy making.

• Monitoring Technology. Testing the impact of advanced remote sensing technologies to help regulators tackle air pollution.

This is applied research with real regulatory partners and real-world implications, offering fellows a direct line of sight from rigorous analysis to environmental policy at scale.

The Health Lab partners with civic and community leaders to identify, rigorously evaluate, and scale programs and policies that improve health outcomes and address barriers to health equity.

Fellows at the Health Lab will contribute to that work across two high-impact research portfolios: transforming how the nation's 911 system responds to crisis calls and improving housing and health outcomes for people with complex needs.

Emergency Crisis Response. Through its emergency crisis response portfolio, the Health Lab leads innovative studies to address systemic issues that the U.S. 911 system faces and that disproportionately affect communities of color by reducing inequitable and harmful first responses, advancing analytic methods in 911, rigorously evaluating new programs and policies, and supporting the professionals who perform this essential work. This work includes randomized controlled trials testing how bias, heuristics, and cognitive load affect call classification, and national surveys and case studies to build a comprehensive dataset on 911 and other three-digit service lines.

Fellows can expect to support these projects with:

• Research Design and Analysis. Identifying analytical approaches to research questions, running sampling and randomization across multiple projects, and supporting quality control of data collection.

• Data and Survey Management. Overseeing the creation and maintenance of survey instruments in REDCap.

• Research Communication. Communicating research to both technical and non-technical audiences, including policymakers, academic researchers, and community stakeholders.

This is rigorous, applied research with direct implications for how communities respond to crisis and how systems serve those most at risk. 

The Mansueto Institute supports researchers with solving some of the most complex challenges facing cities today. With the rise of global urbanization, this work is more vital than ever. Their strategic approach involves impactful research, inclusive partnerships, and transformative events and educational opportunities.

Fellows at the Mansueto Institute may contribute to this work across two current areas of focus:

• Local Data Journalism. The Local Data Journalism initiative brings together data scientists, journalists, and faculty to produce high-quality, investigative stories on critical issues impacting cities, using the highest caliber analytics tools and approaches. Fellows will collaborate on projects that bridge the gap between academic research and public awareness, contributing to data-driven journalism pieces that engage broad audiences and reach major media outlets.

• Property Tax Fairness. As the single largest source of local taxes for cities, counties, school districts, and special districts, property taxes impact everyone. Yet, property taxes are inequitable across many cities: low-value properties face higher tax assessments, relative to their actual market values, than do high-value properties. This tax regressivity disproportionately burdens lower income residents. To better understand these issues, the Property Tax Fairness project has reviewed millions of sales records for properties throughout the country to present the results of these evaluations. After an analysis released by the Mansueto Institute, the Detroit City Council passed two resolutions to halt foreclosures and correct property tax assessments on that city’s lowest priced homes. A similar analysis conducted in Cook County, Illinois spurred significant reforms by the county assessor that greatly improved property tax fairness.

Fellows can expect to support projects with:

• Modeling and Reproducible Code: Building statistical models across a range of projects and writing well-documented computer programs that ensure analyses are reproducible and accessible to collaborators.

• Visualization and Research Communication: Creating data visualizations and translating research for audiences ranging from policymakers and journalists to academic researchers.

• Data Infrastructure and Analysis: Processing large and complex research and administrative datasets, building and maintaining automated data pipelines and statistical analysis, and applying statistical and machine learning methods to urban research questions.

This is applied urban research with demonstrated policy impact, offering fellows the opportunity to see rigorous data work translate directly into change in cities across the country.